Perform overrepresentation analysis for a set of genes

Usage

enrichment_analysis(
  genes,
  background_genes,
  annotation,
  column = NULL,
  correction = "BH",
  p = 0.05,
  min_setsize = 10,
  max_setsize = 500,
  bp_param = BiocParallel::SerialParam()
)

Arguments

genes: Character vector containing genes for overrepresentation analysis.
background_genes: Character vector of genes to be used as background for the overrepresentation analysis.
annotation: Annotation data frame with genes in the first column and functional annotation in the other columns. This data frame can be exported from Biomart or similar databases.
column: Column or columns of annotation to be used for enrichment. Both character or numeric values with column indices can be used. If users want to supply more than one column, input a character or numeric vector. Default: all columns from annotation.
correction: Multiple testing correction method. One of "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr" or "none". Default is "BH".
p: P-value threshold. P-values below this threshold will be considered significant. Default: 0.05.
min_setsize: Numeric indicating the minimum gene set size to be considered. Gene sets correspond to levels of each variable in annotation). Default: 10.
max_setsize: Numeric indicating the maximum gene set size to be considered. Gene sets correspond to levels of each variable in annotation). Default: 500.
bp_param: BiocParallel back-end to be used. Default: BiocParallel::SerialParam()

Value

A data frame of overrepresentation results with the following variables:

term: character, functional term ID/name.
genes: numeric, intersection length between input genes and genes in a particular functional term.
all: numeric, number of all genes in a particular functional term.
pval: numeric, P-value for the hypergeometric test.
padj: numeric, P-value adjusted for multiple comparisons using the method specified in parameter adj.
category: character, name of the grouping variable (i.e., column name of annotation).

Author

Fabricio Almeida-Silva

Examples

# \donttest{
data(filt.se)
data(zma.interpro)
genes <- rownames(filt.se)[1:50]
background_genes <- rownames(filt.se)
annotation <- zma.interpro
# Using p = 1 to show all results
enrich <- enrichment_analysis(genes, background_genes, annotation, p = 1)
# }